In the pre-training stage, the embedding dimension of the CGCNN encoder is set to 128. We use the Adam optimizer[1]} with a learning rate of \(0.00001\)  and a batch size of 64 and pre-train the model for 15 epochs. The other hyperparameters for the CGCNN model are kept the same as in the original paper. The train/validation ratio for pre-training data is 95%/5%. For pre-training, we combine the datasets from the Matminer database [2]} and the hypothetical Metal-Organic Framework dataset[3]}, aggregating a total of 428,275 samples. The labels in the datasets are not used during CT pre-training. In the fine-tuning stage, we add a randomly initialized MLP head with two fully connected layers to generate the final property prediction. The model in the fine-tuning stage is trained for 200 epochs. Similarly, the results for supervised CGCNN are also reported after training the model for 200 epochs. For fine-tuning we use the Adam optimizer with a learning rate of \(0.001\)  and batch size 128.
